1. Field of the Invention
The present invention relates to a signal processing system and a signal processing method and, more particularly, to a physiological signal processing system and a method for filtering noise generated by the same.
2. Description of the Related Art
Because of rapid technological advancement, some physiological states of human body can be analyzed and monitored through wearable devices, which are oftentimes applied to areas of exercise, medical treatment, sleep and car driving. For example, when a user wears a smart watch equipped with optical sensors, the smart watch senses user's activities upon exercising or walking, detects user's physiological signals, such as photoplethysmogram (PPG) signals, and analyzes the physiological signals to acquire physiological parameters in association with user's heart rate variability (HRV). The more correct the acquired physiological signals are, the more accurate the user's HRV is.
When conventional smart watches are used to monitor users' PPG signals, optical leakage easily arises from vibration, noise and users' body movement and causes distorted PPG signals, rendering the PPG signals inaccurate. Moreover, PPG signals tend to have large quantity of data and conventional methods for analyzing PPG signals are too complicated and time-consuming to achieve real time processing of PPG signals. As disclosed in Taiwan patent publication no. 201511735, entitled “A PPG-based physiological sensing system with spatio-temporal sampling approach toward identifying and removing motion artifacts from optical signal” (hereinafter “the conventional system”), the PPG-based physiological sensing system is mainly applied to technical fields of fitness and/or exercise to realize stabilization and accurate determination of PPG signals. According to the PPG-based physiological sensing system, a spatio-temporal sampling approach toward identifying and removing motion artifacts from optical signal is employed. During each state of body movement, the optical signal can be instantaneously received by a wearable optical sensing device. The techniques available to digital signal processing for the conventional system include Kalman filter, Fourier analysis, peak value identification and independent component analysis (ICA). The optical signals received over time are processed to identify and remove motion artifacts from the optical signals, such that the physiological parameters can be accurately determined to restore the real PPG signals and inaccuracy resulting from motion artifacts in the physiological sensing device can be resolved.
As can be seen from the foregoing conventional system, wearable devices can be used to monitor users' physiological signals. Higher accuracy of acquired physiological signals ensures more correct determination of users' physiological states. Upon detection of users' physiological signals, physiological signals are prone to distortion because of vibration, noise and users' body movement. In view of large quantity of data, enhanced accuracy of physiological signals requires time-consuming analysis and computations. Despite a bunch of digital signal processing techniques for improvement of signal accuracy, complicated mathematical analysis method is not only time-consuming but also fails to meet the demand of instant processing. Especially for vehicle driving, if intending to get rid of noise for signal restoration using complicated digital signal processor, it is impossible to attain instant physiological analysis of drivers for the sake of long computation time required.